How can hotels improve personalized service?

Since the beginning of 2023, the market economic heat of the hotel service industry has been high:

The recovery in travel spending has led to a spike in hotel occupancy

According to data from Meituan and Dianping, since February this year, outcall massage hong kong the national accommodation industry has continued the good recovery momentum of the Spring Festival holiday, reaching a peak on several consecutive weekends.

[Central No. 1 Project Document can promote the upgrading of rural homestays]

Document No. 1 of the Chinese People's Government in 2023 proposes the implementation of high-quality rural leisure tourism projects to improve the quality of rural home accommodation. This is the second consecutive year that rural residents' housing has been written into the central No. 1 document, and rural residents' housing has been positioned as a "new rural product and new business form."

[Strong business travel demand]

Compared with leisure tourism, the demand for business activities is stronger and the demand for business tourism is more stable.

However, under the influence of changes in consumer habits, hotel business process reengineering and technological progress in the post-pandemic era, outcall massage services the boundaries of enhancing the core competitiveness of hotels are constantly widening:

[Hotel service industry chain development trend is significant, multicultural brand matrix focus on high-end]

The epidemic has accelerated the cleaning of single hotels, and the group heating rate under the industry chain has increased significantly, in this context, the market share of hotels leading the trend of expansion is growing.

[Brand recognition and loyalty become barriers to industry competition]

Brand awareness will bring consumers higher expectations of accommodation experience and influence their choice. Building a membership system with high loyalty can promote brand upgrading, but the cost is higher.

[Hotels need to adapt to customers' growing demand for personalized experiences]

The hotel environment, service social experience is the foundation of the hotel management industry, but our new generation of mainstream customers are more inclined to personalized, diversified development needs, they can define the hotel is no longer as a simple business travel, lodging and eating as one of the places. It has become a trend for hotels to continuously polish "personalized learning experience" with the help of artificial intelligence technology.

"Both" to enhance the customer experience

Whether from the perspective of industry trends, the necessity of brand development or the urgent needs of customer groups, the necessity of hotel experiential management has been highlighted.

In years of practice, we have found that the awareness of hotels using intelligent and digital means for experience management has been awakened, and digital customer experience has gradually become a new battlefield of competition in the hotel industry. However, in the process of digitization, we also found some common problems: scattered service items, multiple management levels, and prominent consumer personalized needs.

The service process is decentralized and the management object is complex

Pain point

【 Coarse granularity management 】

There are many touch points that affect the consumer's hotel experience, and the interaction between the hotel and it has already occurred from the beginning of the consumer's demand for booking the hotel. Hotel customer experience management presents the development trend of scene and fragmentation, service projects are scattered, contact points are many, and hotel management is difficult.

[Relying too much on artificial intelligence for network public opinion platform data collection + telephone return visit]

It takes a lot of manpower to manage the public opinion of the external OTA platform of the system, and the timeliness is poor, and it is easy to form a data information island within the enterprise company, and there is a lack of relevant professional team to do data mining and analysis.

solution

internal business process, according to the guest check-in process, draw the experience journey.

The organization of the enterprise should not only focus on the hotel lobby (such as the front desk, concierge, etc.), rooms, kitchens, cleaning and other non-direct customer departments, which are still the key links affecting the customer experience.

In the era of mobile Internet, the interactive journey between customers and hotels has spanned the entire life cycle. From the customer's initial awareness of the hotel brand, contact with the hotel booking, check-in, check-out, re-check-in, etc. Each link involves a variety of scenarios and contacts.

Hotel development needs to establish a self-driven process from measurement, tracking, optimization to monitoring by establishing a working mechanism to track and manage corporate customer service experience, with the goal of continuously providing customers with excellent experience.

Therefore, to comprehensively sort out the customer journey, it is not only necessary to study from the perspective of the entire life cycle, but also to consider the granularity of different scenarios and divide the levels of the customer journey.

How to organize the complete customer journey? You can click to look back: "Is the customer journey map difficult to organize? After reading these three points, it is simple"

(Betmarket System schematic)

Combine business data with empirical data around satisfaction and the National Bureau of Statistics, focusing on external voices

【 Build index system 】

The scientific index system will provide guidance for enterprises to carry out quantitative analysis of customer experience in an orderly manner and coordinate resource rectification. At present, the mainstream practice of industrial enterprises often takes NPS as the north star index and builds a multi-level monitoring index system.

The first-level evaluation index is the North Star index, which is generally a North star index combining the customer service experience learning journey for satisfaction and comprehensive practical feelings;

The second level is the Travel Index, which is set according to the stages of the trip and covers all the stages before, during and after the stay, plus the overall sensation index of the experience

The three-level index is the scene contact index at each stage of the journey.

The four-level index is the factor index that affects the scene contact point.

[Automatic and continuous complete experience monitoring]

By building a questionnaire matrix and combining with the map of enterprise customer journey, we teachers are required to obtain direct feedback from customers through questionnaire delivery at some key nodes of issues. At this time, the recovered questionnaires can be classified into known customer journey contacts. The whole teaching process can not only be automated and continuous through the digital customer experience economy management work platform.

The integration of O-Data into the indexing system will also help pinpoint the drivers that impact the customer experience.

[Automatic management of external public opinion]

In addition, under the digital foundation, hotel public opinion information is becoming more and more important, diversified OTA platforms and rating information become the decisive factor for customers to choose whether to register or not, traffic and word-of-mouth management is indispensable.

By connecting with multiple data sources such as OTA public opinion platforms, the omnichannel guest experience data collection is realized.

With the help of electronic computer NLP intelligence, text data analysis and research ability, automatically extract topic distribution and emotion classification, so that the frequency of network social public opinion information collection can be as fast as half an hour, and the earliest warning push can be 10 minutes after collection. Through unsupervised learning text mining, students can identify the facts, relationships and opinions buried in a large number of texts, discover the potential risks of enterprises themselves, and make in-depth comparisons with competitive products.

After extracting the captured information and transforming it into a structured form, it can be used for further analysis and insight into the data.